Defining and delivering personalized entity recommendations

ABSTRACT

Systems, methods, and computer-readable media are provided for defining personalized entity recommendations during a WYSWYG authoring experience and delivering such personalized entity recommendations. At the time of authoring, a user selects a baseline entity on a webpage where the personalized entity recommendation is to be delivered. A HTML DOM of the selected baseline entity is parsed into a plurality of HTML elements. Entity attributes associated with a recommended entity are mapped to the parsed HTML DOM generating an entity recommendation definition and the entity recommendation definition is presented enabling a user (e.g., a recommendation author) to update, edit, and/or approve the same. At runtime (i.e., at the time an entity recommendation is delivered), the HTML DOM of the selected baseline entity is cloned and the entity recommendation definition is utilized to populate entity attributes associated with the recommended entity into the HTML DOM creating a personalized entity recommendation. The personalized entity recommendation then is delivered to the destination webpage independent of the destination webpage characteristics.

BACKGROUND

With the rapid growth of online marketing (e.g., e-marketing) and onlinesales (e.g., e-sales) used as tools to promote items of potentialinterest to viewers (e.g., potential customers), the need for web-basedpersonalization of recommended entities in terms of definingcustomer-centric content and dynamic content delivery has increased.With the proliferation of various web frameworks and Internet browserplatforms on which web-based personalized entity recommendations areconsumed, authors of personalized entity recommendations (e.g., entitymarketers, advertisers, promoters, and the like) desire to ensure thepersonalized recommendations they author can be easily presented acrossvarious web platforms and user devices, independent of characteristicsof the destination webpage (i.e., the webpage on which a personalizedrecommendation is to be presented) such as structure, style, layout,view, and the like, in order to reach maximum viewership.

To cater to the increased demand for personalized entityrecommendations, authors of such recommendations oftentimes userecommendation authoring systems to define and deliver recommendationspersonalized with regard to a target customer or potential customer.What You See is What You Get (“WYSWYG”), an authoring experience thatenables authors to preview expected content output as the content isbeing generated, is one such authoring tool recommendation authors canuse to define and deliver personalized entity recommendations.

SUMMARY

Embodiments of the present invention relate to, among other things,methods, systems, and computer-readable storage media for definingpersonalized entity recommendations during a WYSWYG authoringexperience. In this regard, a baseline entity on a destination webpagecan be selected as a foundation for generating entity recommendationdefinitions that can be utilized to generate personalized entityrecommendations. Once a baseline entity is selected from a destinationwebpage, an identifier of the baseline entity can be recorded for use atthe time a personalized entity recommendation is delivered. Anintelligent WYSWYG recommendation authoring system in accordance withimplementations of the present invention can parse the HTML documentobject model (“DOM”) of the baseline entity into one or more HTMLelements. One or more entity attributes associated with a recommendedentity can be mapped to the parsed HTML DOM generating an entityrecommendation definition. Entity attributes mapped to the parsed HTMLDOM of the baseline entity can be derived from a catalog associated withthe recommended entity, for instance, if the baseline and/or recommendedentity is present in the entity catalog. Alternatively, entityattributes mapped to the parsed HTML DOM of the baseline entity can bedetermined via a heuristic approach, whereby attributes are mapped basedon the mapping of HTML classes and elements, for instance, if thebaseline and/or recommended entity is not present in the entity catalog.The entity recommendation definition then can be presented for editing,updating and/or approval.

Embodiments of the present invention further relate to, among otherthings, methods, systems, and computer-readable storage media fordelivering personalized entity recommendations independent ofcharacteristics of the destination webpage. Once an entityrecommendation definition is completed, the entity recommendationdefinition is maintained at an edge server. At runtime, a deliverylibrary can access the entity recommendation definition from the edgeserver, clone the HTML DOM of the baseline entity identified in theentity recommendation definition on the destination webpage and use themapped entity attributes associated with the entity recommendationdefinition to populate entity attributes associated with the recommendedentity into the cloned HTML DOM generating a personalized entityrecommendation. The personalized entity recommendation then can bedelivered for presentation in association with the destination webpageindependent of destination webpage characteristics.

Advantageously, intelligent personalized entity recommendation authoringand delivery in accordance with some implementations of the presentinvention can alleviate the need to store entity recommendationtemplates on a recommendation server, eliminate the delivery ofrecommendation content from server to client, alleviate maintenance ofrecommendation templates, for instance, when the structure and/or styleof a destination webpage changes, optimize content delivery, and reducethe cost of recommendation delivery by using already painted DOMs ratherthan injecting new HTML. Further, personalized entity recommendation anddelivery in accordance with some implementations of the presentinvention reduces recommendation load time chances of flicker duringdelivery of entity recommendations, can work across different browsersand various web frameworks, and is not limited to desktop views but canbe extended to mobile and other user device views.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is a schematic depiction of a system for facilitating authoringof personalized entity recommendations, in accordance with someimplementations of the present disclosure;

FIG. 2 is a block diagram depicting a recommendation engine, inaccordance with some implementations of the present disclosure;

FIG. 3 is a schematic diagram showing an exemplary user interfaceillustrating a baseline entity selection in association with adestination webpage, in accordance with some implementations of thepresent disclosure;

FIG. 4 is a schematic diagram showing an exemplary user interfaceillustrating attributes associated with a recommended entity, theattributes capable of being mapped to a HTML DOM, in accordance withsome implementations of the present disclosure;

FIG. 5 is a schematic representation of computer-readable instructionsshowing an entity recommendation definition, in accordance with someimplementations of the present disclosure;

FIG. 6 is a schematic representation of computer-readable instructionsshowing a stored entity recommendation definition populated with entityattributes associated with a recommended entity, in accordance with someimplementations of the present disclosure;

FIG. 7 is flow diagram showing an exemplary method for defining apersonalized entity recommendation, in accordance with someimplementations of the present disclosure;

FIG. 8 is flow diagram showing an exemplary method for delivering apersonalized entity recommendation, in accordance with someimplementations of the present disclosure; and

FIG. 9 is a block diagram of an exemplary computing environment suitablefor use in accordance with some implementations of the presentdisclosure.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject matteralso might be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

For purposes of this disclosure, the word “including” has the same broadmeaning as the word “comprising.” In addition, words such as “a” and“an,” unless otherwise indicated to the contrary, include the plural aswell as the singular. Thus, for example, the requirement of “a feature”is satisfied when one or more features are present. Also, the term “or”includes the conjunctive, the disjunctive and both (a or b thus includeseither a or b, as well as a and b).

Various terms are used throughout this description. Definitions of someterms are included below to provide a clearer understating of the ideasdisclosed herein:

As used herein, the term “entity” refers to a product, good, service,and the like that may be offered, advertised, promoted, or suggested totarget customers or potential customers. By way of example and notlimitation, entities can include movies, videos, eBooks, music,clothing, electronics, moving services, cleaning services, cookingservices, and the like.

As used herein, the term “personalized entity recommendation” refers toan entity offering, advertisement, promotion, suggestion, or the likethat is tailored with regard to target customers and/or potentialcustomers. Entity recommendations can be personalized with regard totarget customers based on a variety of factors. By way of example onlyand not limitation, factors that may be used in determining whichentity/entities to recommend can include, e.g., purchase history, brandpreference, customer location, travel history, social network activity,and the like. Various algorithms are known to those having ordinaryskill in the art for determining which entities to recommend to targetcustomers (and/or potential customers) and, accordingly, suchdetermination is not further described herein. In the context ofe-marketing, personalized entity recommendations are entity offerings,advertisements, promotions, suggestions and the like that are tailoredto target customers or potential customers and that are defined,delivered, and consumed electronically.

As used herein, the term “baseline entity” refers to an entity presentedin association with a destination webpage that is selected by arecommendation author to be used as the foundation to generate entityrecommendation definitions. By way of example only and not limitation, abaseline entity may be a particular mobile device featured among manyother mobile devices located on a webpage detailing various mobiledevices for sale by various manufacturers and mobile carriers.

As used herein, the term “entity recommendation definition” refers tothe parsed HTML DOM elements of a baseline entity having attributes of arecommended entity mapped to at least a portion thereof. That is, anentity recommendation definition is the HTML DOM associated with abaseline entity having at least a portion of the corresponding HTMLelements populated with an entity attribute of a recommended entity thatcorresponds to the appropriate attribute category. By way of exampleonly, parsed HTML DOM containing a single computer-readable instructionthat reads “entityAttribute: ‘entity.title’” becomes an entityrecommendation definition once an intelligent WYSWYG authoring system inaccordance with some implementations of the present disclosure maps thetitle attribute of the recommended entity to the parsed HTML DOM,replacing “entityAttribute: ‘entity.title’” with “attribute: ‘title’”.FIG. 5 is one example of a recommendation definition represented in JSONformat. It will be understood by those having ordinary skill in the artthat parsed HTML DOM may contain various numbers of entity attributesand the example provided herein is by way of illustration only. Inaspects hereof, entity recommendation definitions are maintained at edgeservers to be accessed at runtime (that is, when a personalized entityrecommendation is to be delivered).

As used herein, the term “entity attribute” refers to a characteristicof an entity. By way of example only and not limitation, entityattributes may include an entity's image, title, price, height, width,weight, customer satisfaction rating, and the like.

As used herein, the term “destination webpage” refers to a webpage wherea personalized entity recommendation is to be delivered. By way ofexample only and not limitation, a destination webpage may include anywebpage on which a personalized recommendation author wishes to promotea personalized entity recommendation campaign. A destination webpage caninclude, by way of example only and not limitation, the homepage of apersonalized browser, the footwear webpage of a shoe manufacturer'swebsite, the checkout webpage of an online purchase and deliveryservice, or the FAQ section of a travel destination website.

As used herein, the term “destination webpage characteristic” refers toany one of the configuration, organization, and/or arrangement thereof.By way of example only and not limitation, a destination webpagecharacteristic can refer to the webpage's structure, style, layout, orview.

Personalized entity recommendations are generally authored for differentpurposes. For example, personalized entity recommendations may beauthored to promote special offers regarding mobile devices andassociated monthly data plans in an attempt to increase a mobilecarrier's customer base. Personalized entity recommendations also mayinclude an advertisement for particular clothing item or wearabletechnology from a department store presented to increase sales and/orawareness. Personalized entity recommendations further may encompassseasonal or regional promotions for various vacation and/or traveldestinations during peak-season and during off-season. In preparing suchpersonalized entity recommendations, an author (e.g., marketer) may wishto ensure their entity recommendation campaign reaches the maximumnumber of viewers, regardless of the web browser, network speed, and/oruser devise on which a potential customer views the personalized entityrecommendation.

Prior attempts have been made to create an authoring experience thateasily and intuitively allows a marketer to define and deliverpersonalized entity recommendations to various browsers and acrossmultiple web frameworks. These methods, however, have their limitationsincluding large maintenance overhead, dependency on destination webpagedevelopers each time a webpage's characteristics change, slow deliveryruntime, and webpage flicker.

For example, some current recommendation definition and delivery systemsretain the Hypertext Markup Language (HTML) content and destinationwebpage characteristics as part of the entity recommendation definition,thereby restraining the personalized recommendation to depend on thestructure, style, layout and/or view of where specifically thepersonalized recommendation is being delivered (i.e., destinationwebpage). This method of personalized entity recommendation definitionand delivery is further restrictive in that it requires entityrecommendation authors to change the entity recommendation definitioneach time a change is made to one or more characteristics of thedestination webpage.

It would be beneficial if entity recommendation authors had arecommendation generating tool that enabled them to define and deliverrecommendation campaigns across multiple destination webpages andchannels, to be viewed on various user devices with various networkspeeds, without redefining the personalized entity recommendation foreach instance of the destination webpage and/or each user device.

Accordingly, embodiments described herein are directed to defining apersonalized entity recommendation deliverable to a destination webpageindependent of destination webpage characteristics. More generally,embodiments described herein are directed to defining a personalizedentity recommendation by selecting a baseline entity on a destinationwebpage visible in an authoring mode during a WYSWYG authoringexperience, recording an identifier of the baseline entity, parsing theHTML DOM associated with the baseline product into a plurality of HTMLelements, mapping entity attributes associated with a recommended entityto the parsed HTML elements and generating an entity recommendationdefinition utilizing the identifier of the baseline entity and themapped entity attributes. Entity attributes may be sourced from knownentities in an entity catalog, or from unknown entities not present inan entity catalog by way of a heuristic approach. By way of example, ifthe entity is a product, the product attributes may be sourced fromknown products in a product catalog, or from unknown products notpresent in a product catalog by way of a heuristic approach.

By mapping entity attributes from already present entities in an entitycatalog, or from unknown entities by way of a heuristic approach, theintelligent WYSWYG system can learn the entity attributes to HTMLelements mapping over time. In other words and by way of example, if aproduct catalog is associated with a specific destination webpage, theproduct attributes once mapped can be reused, such as when a marketerdefines additional personalized entity recommendations. In this regard,the intelligent WYSWYG system can learn from the stored attributemapping to make the visual authoring experience for an author (e.g.,marketer, promoter, advertiser, etc.) easier and more intuitive. Suchmachine learning methods are known and apparent to those having ordinaryskill in the art to which the present disclosure pertains. Upon mappingentity attributes associated with a recommended entity to the parsedHTML DOM, the intelligent WYSWYG system can present the entityrecommendation definition, enabling updating, editing, and/or approvalthereof.

During delivery of a personalized entity recommendation, currentpersonalization engines merge personalized content and an HTML template,and inject dynamic content onto the destination webpage as the pageloads. Further, the HTML DOM rendering with current methods is notoptimized since web browsers paint the personalized recommendation inits entirety rather than reusing existing HTML DOMs already rendered onthe destination webpage. This increases the latency of the runtimerecommendation delivery, in some cases causes flickering, and results ina poor and/or slow user experience for the personalized entityrecommendation viewer.

Accordingly, embodiments described herein are directed to delivering apersonalized entity recommendation independent of destination webpagecharacteristics. In this regard, embodiments described herein utilize anentity recommendation to clone, at runtime, the HTML DOM of a baselineentity on a destination webpage and populate the entity attributesassociated with a recommended product, good, service, or the like intothe cloned HTML DOM, thereby generating the personalized entityrecommendation for presentation on the destination webpage. Inoperation, embodiments described herein leverage the browser capabilityof cloning already painted DOMs quickly and thereby reduce thelikelihood of flicker and/or bad user experience frequently seen inother methods due to eliminating the need for complete replacement ofHTML content at runtime.

Turning now to FIG. 1, a block diagram is provided illustrating anexemplary system 100 for authoring personalized entity recommendationsin association with a destination webpage, independent of thecharacteristics thereof, in accordance with implementations of thepresent disclosure. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, and groupings of functions, etc.) can be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed by oneor more components may be carried out by hardware, firmware, and/orsoftware. For instance, various functions may be carried out by aprocessor executing instructions stored in memory.

Among other components not shown, the system 100 includes arecommendation engine 102, a user device 104, a data store 106, and anedge server 110. It should be understood that the system 100 shown inFIG. 1 is an example of one suitable architecture for implementingcertain aspects of the present disclosure. Each of the components shownin FIG. 1 may be implemented via one or more computing devices, such ascomputing device 900 described with reference to FIG. 9, for example. Itshould be noted that implementations of the present disclosure areequally applicable to mobile computing devices and devices acceptinggesture, touch, and/or voice input. Any and all such variations, and anycombination thereof, are contemplated to be within the scope ofimplementations of the present disclosure.

As shown in FIG. 1, the recommendation engine 102 and the user device104 may communicate with each other via a network 108, which mayinclude, without limitation, one or more local area networks (LANs)and/or wide area networks (WANs). Such networking environments arecommonplace in offices, enterprise-wide computer networks, intranets,and the Internet. Accordingly, the network 108 is not further describedherein. It should be understood that any number of recommendationengines and/or user devices may be employed within the system 100 withinthe scope of implementations of the present disclosure. Each maycomprise a single device or multiple devices cooperating in adistributed environment. For instance, the recommendation engine 102could be provided by multiple server devices collectively providing thefunctionality of the recommendation engine 102 as described herein.Additionally, other components not shown also may be included within thenetwork environment.

The recommendation engine 102 has access to at least one data store orrepository 106 that includes data specific to a plurality of products,goods, and/or services. In implementations of the present disclosure,the data store is configured to be searchable for one or more of theitems stored in association therewith. It should be understood that theinformation stored in association with the data store may beconfigurable and may include any information relevant to, by way ofexample only, entity catalogs that contain known entities and theirassociated entity attributes (e.g., image, title, customer satisfactionrating, price, height, width, weight, and the like) that may be used,for example, to replace existing entity attributes in cloned HTML DOMs.As described in more detail below, the data store 106 may include entitycatalogs containing entities related to the electronic, fashion,automotive, education, and sports industries, or the like, and/ormetadata associated therewith. Such entity catalog and entity attributedata may be stored in the data store 106 and accessible to any componentof the system 100. The content and volume of such information are notintended to limit the scope of aspects of the present technology in anyway. Further, the data store 106 may be a single, independent component(as shown) or a plurality of storage devices, for instance, a databasecluster, portions of which may reside in association with therecommendation engine 102, another external computing device (notshown), and/or any combination thereof. Additionally, the data store 106may include a plurality of unrelated data repositories or sources withinthe scope of embodiments of the present technology.

The data store also may be updated at any time, including an increase ordecrease in the amount of entity catalog and entity attribute datarelevant to any given personalized entity recommendation campaign or anincrease or decrease in the amount of entity catalog and entityattribute data irrelevant to any given personalized entityrecommendation campaign.

The recommendation engine 102 has access to at least one edge server 110which maintains completed entity recommendation definitions. Inimplementations of the present disclosure, the edge server is configuredto receive a completed entity recommendation definition from arecommendation engine, such as recommendation engine 200 of FIG. 2 andmaintain the entity recommendation definition. At runtime, a deliverylibrary can access the entity recommendation definition from the edgeserver, clone the HTML DOM of the baseline entity identified in theentity recommendation definition on the destination webpage, and use theentity attributes associated with the entity recommendation definitionto populate entity attributes associated with the recommended entityinto the cloned HTML DOM generating a personalized entityrecommendation. The personalized entity recommendation then can bedelivered for presentation in association with the destination webpageindependent of destination webpage characteristics. The content andvolume of such entity recommendation definitions are not intended tolimit the scope of aspects of the present technology in any way.Further, the edge server 110 may be a single, independent component (asshown) or a plurality of servers. Additionally, the edge server 110 mayinclude a plurality of unrelated information within the scope ofembodiments of the present technology.

Generally, the system 100 is configured to facilitate the authoring ofpersonalized entity recommendations during a WYSWYG authoring experiencedeliverable independent of destination webpage characteristics (e.g.,structure, style, layout, view, and the like). At a high-level, HTML DOMof a selected baseline entity located on a destination webpage is parsedinto a plurality of HTML elements. Entity attributes associated with arecommended entity are mapped to the parsed HTML DOM, thereby creating,in association with an identifier of the baseline entity, an entityrecommendation definition. A visual representation of the mapped entityattributes is presented for manual editing, updating, and/or approval.The entity recommendation definition is maintained on an edge server foruse at runtime. At runtime, delivery libraries clone the HTML DOM of theselected baseline entity on the destination webpage, and, using theentity recommendation definition maintained at the edge server, populatethe cloned HTML DOM with the entity attributes of the recommendedentity. In this regard, populating the cloned HTML DOM with the entityattributes of the recommended entity replaces already existing entityattributes contained within the cloned HTML DOM, thereby delivering thepersonalized entity recommendation to the destination webpageindependent of the webpage characteristics of the destination webpage.

Returning to FIG. 1, in operation, the user device 104 is configured toaccess the recommendation engine 102 over the network 108 (e.g., a LANor the Internet). For instance, the user device 104 may be configured toprovide and/or receive data from the recommendation engine 102 via thenetwork 108. The user device 104 may be any computing device that iscapable of facilitating a user to select a baseline entity. Anidentifier of the selected baseline entity can be recorded so that thesame can be used to generate an entity recommendation definition whichcan, in turn, be utilized to recall the identity of the baseline entityat the time of personalized entity recommendation delivery. Any type ofuser interface may be used to facilitate selection of the baselineentity. In some cases, a user may select a baseline entity, for example,by clicking on the entity presented on a destination webpage while in anauthoring mode.

The user device 104 further may be configured to obtain and present anentity recommendation definition, or portion thereof, for editing,updating and/or approval. In this regard, an entity recommendationdefinition generated in response to selecting a baseline entity,recording an identifier of the selected baseline entity, and mappingentity attributes to the parsed HTML DOM of the baseline entity togenerate an entity recommendation definition can be provided to the userdevice for display to a user (e.g., via a browser or applicationinstalled on the user device 104).

In some cases, the user device 104 accesses the recommendation engine102 via a web browser, terminal, or standalone PC application operableon the user device. The user device 104 might be operated by anadministrator, which may be an individual(s) that manages contentassociated with a document, a website, an application, or the like. Forinstance, a user may be any individual, such as a marketer, publisher,or advertiser associated with an entity attempting to define and delivera personalized entity recommendation campaign (e.g., via the Internet).While only one user device 104 is illustrated in FIG. 1, multiple userdevices associated with any number of users may be utilized to carry outembodiments described herein. The user device 104 may take on a varietyof forms, such as a personal computer (PC), a laptop computer, a mobilephone, a tablet computer, a wearable computer, a personal digitalassistant (PDA), an MP3 player, a global positioning system (GPS)device, a video player, a digital video recorder (DVR), a cable box, aset-top box, a handheld communications device, a smart phone, a smartwatch, a workstation, any combination of these delineated devices, orany other suitable device. Further, the user device 104 may include oneor more processors, and one or more computer-readable media. Thecomputer-readable media may include computer-readable instructionsexecutable by the one or more processors.

The recommendation engine 102 generally is configured to definepersonalized entity recommendations during a WYSWYG authoring experienceand deliver the same independent of destination webpage characteristics.In particular, the recommendation engine 102 is configured to receiveselection of baseline entities, record identifiers of the selectedbaseline entities, parse the HTML DOM of selected baseline entities, mapentity attributes associated with recommended entities the parsed HTMLDOM of selected baseline entities, generate entity recommendationdefinitions utilizing the recorded baseline entity identifiers and themapped entity attributes, and deliver personalized entityrecommendations to destination webpages independent of destinationwebpage characteristics. In implementation, and at a high-level, therecommendation engine 102 is configured to receive an indication of auser-selected baseline entity from a destination webpage while in anentity recommendation authoring mode. In embodiments, the recommendationengine 102 is configured to record an identifier of the selectedbaseline entity so that the same can be used at time of delivery of anentity recommendation. The recommendation engine 102 further isconfigured to parse the HTML DOM of the selected baseline entity and mapentity attributes from a desired recommended known or unknown entity tothe parsed HTML DOM. Still further, the recommendation engine 102 isconfigured to generate entity recommendation definitions utilizing therecorded baseline entity identifiers and the corresponding parsed HTMLDOM. The recommendation engine 102 further is configured to visuallypresent entity recommendation definitions for updating, editing, and/orapproval. In implementation (and as more fully described below), atruntime, a delivery library clones the HTML DOM of a selected baselineentity on the destination webpage and, utilizing a corresponding entityrecommendation definition, populates the mapped entity attributescontained within the entity recommendation definition onto the HTML DOMof the cloned baseline entity, thereby delivering the personalizedentity recommendation to the destination webpage independent of webpagecharacteristics.

An exemplary recommendation engine 200 is provided in FIG. 2. As shownin FIG. 2, the recommendation engine 200 includes a recommendationdefining manager 202, and a recommendation delivery manager 204. Therecommendation defining manager 202 generally is configured tofacilitate the defining of a personalized entity recommendation. Therecommendation delivery manager 204 generally is configured to deliver apersonalized entity recommendation to a destination webpage.Advantageously, such a personalized entity recommendation definition anddelivery process enables personalized entity recommendations to bedelivered to a destination webpage independent of the destinationwebpage characteristics.

Although illustrated as separate components of the recommendation engine200, any number of components can be used to perform the functionalitydescribed herein. Further, although illustrated as being a part of arecommendation engine, the components can be distributed via any numberof devices. For example, the recommendation defining manager 202 can beprovided via one device, server, or cluster of servers, while therecommendation delivery manager 204 can be provided via another device,server, or cluster of servers. The components identified herein aremerely set out as examples to simplify or clarify the discussion offunctionality. Other arrangements and elements (e.g., machines,interfaces, functions, orders, and groupings of functions, etc.) can beused in addition to or instead of those shown, and some elements may beomitted altogether. Further, many of the elements described herein arefunctional entities that may be implemented as discrete or distributedcomponents or in conjunction with other components, and in any suitablecombination and location. Various functions described herein as beingperformed by one or more components may be carried out by hardware,firmware, and/or software. For instance, various functions may becarried out by a processor executing instructions stored in memory.

As described, the recommendation defining manager 202 generally isconfigured to facilitate the defining of personalized entityrecommendations. As illustrated, the recommendation defining manager 202includes a baseline entity collecting component 206, a baseline entityidentifier recording component 208, a HTML DOM parsing component 210, anentity attribute mapping component 212, a recommendation definitiongenerating component 214, and a presentation causing component 216.Although illustrated as separate components of the recommendationdefining manager 202, any number of components can be used to performthe functionality described herein.

The baseline entity collecting component 206 is configured to collect orobtain an indication of a user-selected baseline entity from adestination webpage while the associated recommendation system (e.g.,system 100 of FIG. 1) is in an authoring mode. As described, a baselineentity can be any advertised, offered, or promoted item on a destinationwebpage, but is not exclusive to those items, goods, and/or services. Abaseline entity may be collected or obtained in any manner. In somecases, a baseline entity is selected by a user of the recommendationengine 200, such as a marketer, advertiser, or promoter. In this regard,a marketer or set of marketers might select a baseline entity, forexample, via a graphical user interface accessible by way of anapplication on a user device. As an example, a marketer might select abaseline entity via user device 104 of FIG. 1 that is connected to thenetwork 108.

The baseline entity identifier recording component 208 is configured torecord identifiers associated with selected baseline entities. In thisregard, once a baseline entity is selected as the foundation forgenerating a personalized entity recommendation, the baseline entityidentifier recording component 208 records the identifier of thebaseline entity (as part of the entity recommendation definition, asmore fully described below) so that the same can be used at the time ofpersonalized recommendation delivery.

The HTML DOM parsing component 210 is configured to parse the HTML DOMof a selected baseline entity into a plurality of HTML elements. Inembodiments, a selected baseline entity can be parsed to obtain its HTMLDOM. In that regard, the HTML DOM parsing component 210 deconstructs theuser-selected baseline entity into its document object model treestructure such that each object or HTML element in the DOM tree may beaddressed and manipulated.

The entity attribute mapping component 212 is configured to map entityattributes onto the parsed HTML DOM. In embodiments, the entityattribute mapping component 212 maps entity attributes to the HTMLelements of the parsed HTML DOM of the user-selected baseline entitybased on information available in an entity catalog and/or heuristics.In particular, the mapped entity attributes can originate from knownentities located in an entity catalog within a data store, such as datastore 106 of FIG. 1, or from unknown entities using a heuristic approachwherein attributes can be mapped to the parsed HTML DOM based on mappingof HTML classes and elements, and the same can be reused by the entityattribute mapping component 212 while defining further recommendationson the same or related webpages.

By way of example, if a marketer selects a product present in a productcatalog, such as a product catalog located within a data store (such asdata store 106 of FIG. 1), then all or some of the attributes can bereverse mapped to the parsed HTML DOM based on values in the parsed HTMLDOM. Further, by way of example only and not limitation, for a productnot present in the product catalog, attributes can be mapped to theparsed HTML DOM of the baseline product such that an H2 tag for aproduct with class “title” maps to product title within the parsed HTMLDOM, and an IMG tag maps to the parsed HTML DOM of the baseline productto be used as a thumbnail.

As can be appreciated, selection of known and unknown entities canhappen in various ways. In some embodiments, the personalized entityrecommendation author (e.g., marketer, advertiser, promoter, or thelike) can select which entities to map onto the parsed HTML DOM. Inother embodiments, entities may be selected using various knownrecommendation algorithms, a combination of known algorithms, oralgorithms or combinations of algorithms currently unknown in the art.

The entity recommendation definition generating component 214 isconfigured to utilize the baseline entity identifiers and attributes forrecommended entities that are mapped to the HTML DOM of particularbaseline entity identifiers to generate entity recommendationdefinitions. As more fully described below, the entity recommendationdefinitions generated by the entity recommendation definition generatingcomponent 214 may be utilized to generate personalized entityrecommendations in association with destination webpages.

The presentation causing component 216 is configured to causepresentation of the personalized entity recommendation definition forediting, updating, and/or approval. In some cases, the presentationcausing component 216 causes presentation of a personalized entityrecommendation definition that does not require editing and/or updating.In other cases, the presentation causing component 216 causespresentation of personalized entity recommendation definitions that needediting and/or updating. In embodiments, once entity attributes aremapped to the parsed HTML DOM of the user-selected baseline entity, thepresentation causing component 216 causes presentation of the HTML DOMwith the mapped entity attributes. Advantageously, a marketer can thenedit and/or update the attribute mapping and/or approve the attributemapping visually to complete the entity recommendation definition.

As described, the recommendation delivery manager 204 is generallyconfigured to deliver personalized entity recommendations to destinationwebpages by cloning the HTML DOM of selected baseline entities on adestination webpage and populating the cloned HTML DOM with an entityrecommendation definition. As illustrated, the recommendation deliverymanager 204 includes an HTML DOM cloning component 218, and arecommended entity attribute populating component 220. Althoughillustrated as separate components of the recommendation deliverymanager 204, any number of components can be used to perform thefunctionality described herein.

The HTML DOM cloning component 218 is configured to clone the HTML DOMof selected baseline entities on destination webpages. In this regard,the HTML DOM cloning component 218 is configured to clone the HTML DOMof a selected baseline entity on a destination webpage such thatpopulating the entity attributes utilizing the entity recommendationdefinition can occur, thereby delivering the personalized entityrecommendation onto the destination webpage.

The recommended entity attribute populating component 220 is configured,at runtime, to populate the cloned HTML DOM of the selected baselineentity on a destination webpage with the entity attributes containedwithin the entity recommendation definition defined by therecommendation defining manager 202. In particular, the recommendedentity attribute populating component 220 is configured to, at runtime,access the entity recommendation definition and utilize the entityrecommendation definition to populate the recommended entity attributesinto the cloned HTML DOM of the baseline entity on the destinationwebpage thereby delivering the personalized entity recommendation ontothe destination webpage.

Now turning to FIG. 3, a user interface display illustrating arepresentation of baseline entity selection by a user on a destinationwebpage is shown, in accordance with embodiments of the presentinvention. In embodiments, the user interface display 300 is one exampleof many user interface displays that include user interface elementsthat allow for the selection of a baseline entity for use in authoringpersonalized entity recommendations. In particular, the user interfacedisplay 300 is a representation of a baseline entity selection in anauthoring mode during a WYSWYG authoring experience. The user interfacedisplay portion 302 represents a selected baseline entity. Uponselecting the baseline entity, at user interface display portion 304, amenu appears providing the user with options and functionalities anintelligent WYSWYG authoring system in accordance with embodimentshereof can perform. It should be appreciated that the options andfunctionalities presented at user interface display portion 304 are inno way limiting, and that various other options and functionality can bepresented. In operation, once a baseline entity is selected, a userinterface portion, such as user interface display portion 304, can bepresented showing various options and functionalities, including, by wayof example only, “define recommendation mapping,” “replace withrecommendations,” “insert recommendations before,” “insertrecommendations after,” and “expand selection.” At user interfacedisplay portion 306, an option and/or functionality is selected to beperformed by the intelligent WYSWYG authoring system.

Now turning to FIG. 4, a user interface display illustrating arepresentation of attributes of a selected baseline entity that can bemapped, in accordance with embodiments of the present invention, isshown. In embodiments, the user interface display 400 is one example ofmany user interface displays that include user interface elements thatpresent a selected baseline entity, the entity attributes associatedwith the baseline entity, and how they can be mapped via an intelligentWYSWYG authoring system, in accordance herewith. In operation, abaseline entity can be selected, and within that baseline entityattributes can be selected and directed with respect to where they areto be mapped. The user interface display portion 402 represents aselected baseline entity. Upon baseline entity selection, attributes ofthe selected baseline entity can be selected, as represented at userinterface display portion 404. Once the attribute is selected, a userinterface display portion 406 illustrating a menu can be presentedproviding the user with options as to how the entity attribute should bemapped. It should be appreciated that the options and functionalitiesdisplayed at user interface display portion 406 are in no way limiting,and that various other options and functionalities can be presented. Inoperation, once a baseline entity attribute is selected, a userinterface portion, such as user interface display portion 406, can bepresented showing various options and functionalities including, by wayof example only, “map as title,” “map source as page url,” “map src asthumbnail url,” “map as rating,” “map as price,” and “expand selection.”At user interface display portion 406, an option and/or functionality isselected to be performed by the intelligent WYSWYG authoring system inaccordance with embodiments of the present invention.

Now turning to FIG. 5, a schematic representation of computer-readableinstructions showing a stored entity recommendation definition in JSONformat, in accordance with embodiments of the present invention isshown. In embodiments, computer-readable instructions 500 are generatedand stored by a recommendation engine within an intelligent WYSWYGauthoring system, such as recommendation engine 200 of FIG. 2. Thecomputer-readable instructions 500 represent one example of a storedentity recommendation definition. At line 8 of the computer-readableinstructions 500, an intelligent WYSWYG authoring system via an entityattribute mapping component, such as the entity attribute mappingcomponent 212 of FIG. 2, maps the title attribute of a known or unknownentity to be recommended. At line 13 of the computer-readableinstructions 500, an intelligent WYSWYG authoring system via an entityattribute mapping component, such as the entity attribute mappingcomponent 214 of FIG. 2, maps the ‘thumbnailUrl’ attribute of a known orunknown entity to be recommended. At line 18 of the computer-readableinstructions 500, an intelligent WYSWYG authoring system via an entityattribute mapping component, such as the entity product attributemapping component 212 of FIG. 2, maps the rating attribute of a known orunknown entity to be recommended.

It should be appreciated that computer-readable instructions 500 arejust one example of a stored entity recommendation, and entityattributes that can be mapped to the stored entity recommendation fromknown or unknown recommended entities can include more, fewer, the same,or different entity attributes. By way of example only, thecomputer-readable instructions 500 include the title, ‘thumbnailUrl,’and rating attributes. In other stored recommendations, title,‘thumbnailUrl,’ and rating attributes may or may not be included, andother attributes may be included including, but not limited to, image,price, height, width, and weight attributes, and the like.

Now turning to FIG. 6, a schematic representation of computer-readableinstructions showing a stored entity recommendation definition populatedwith recommended entity attributes in JSON format, in accordance withembodiments of the present invention, is shown. In embodiments, thecomputer-readable instructions 600 are generated and stored by arecommendation engine within a WYSWYG authoring system, such as therecommendation engine 200 of FIG. 2. The computer-readable instructions600 represent one example of an entity recommendation definitionpopulated with recommended entity attributes ready for delivery to adestination webpage. At line 9 of the computer-readable instructions600, an intelligent WYSWYG authoring system via a recommended entityattribute populating component, such as the recommended entity attributepopulating component 220 of FIG. 2, populates the title entity attributeof a recommended entity into the stored entity recommendation. At line14 of the computer-readable instructions 600, an intelligent WYSWYGauthoring system via a recommended entity attribute populatingcomponent, such as the recommended entity attribute populating component220 of FIG. 2, populates the ‘thumbnailUrl’ entity attribute of arecommended product into the stored entity recommendation. At line 19 ofthe computer-readable instructions 600, an intelligent WYSWYG authoringsystem via a recommended entity attribute populating component, such asthe recommended entity attribute populating component 220 of FIG. 2,populates the rating entity attribute of a recommended entity into thestored entity recommendation. Lines 22 through 29 of thecomputer-readable instructions 600 are representations of potentialrecommended entities from which a recommended entity attribute mappingcomponent maps recommended entity attributes.

It should be appreciated that computer-readable instructions 600 arejust one example of a stored entity recommendation definition populatedwith recommended entity attributes. By way of example only, thecomputer-readable instructions 600 include the title, ‘thumbnailUrl,’and rating attributes mapped from a recommended entity via a recommendedentity attribute mapping component, such as the recommended entityattribute mapping component 220 of FIG. 2. In other entityrecommendation definitions, title, ‘thumbnailUrl,’ and rating attributesmay or may not be mapped, as well as other entity attributes such asimage, price, height, width, and weight attributes, and the like may ormay not be mapped.

Now turning to FIG. 7, a flow diagram is illustrated showing anexemplary method 700 for defining personalized entity recommendationsdeliverable to destination webpages independent of destination webpagecharacteristics, in accordance with embodiments of the presentinvention. In embodiments, the method 700 is performed by arecommendation engine, such as recommendation engine 200 of FIG. 2.Initially, as indicated at block 702, a selection of a baseline entityassociated with a destination webpage is received. A baseline entity maybe in the form of a product, good, service, or item related to variousindustries, including automotive, fashion, technology, sports, oreducation. The baseline entity includes an associated HTML DOM. Asindicated at block 704, an identifier associated with the selectedbaseline entity is recorded. At block 706, the HTML DOM of a selectedbaseline entity is parsed into a plurality of HTML elements. Asindicated at block 708, one or more entity attributes associated with arecommended entity are mapped to at least a portion of the HTML elementsto generate an entity recommendation definition. Stated differently, atblock 708, entity attributes are mapped to the HTML DOM used to generatethe entity recommendation definition. At block 710, an entityrecommendation definition is generated utilizing the mapped one or moreattributes and the identifier of the selected baseline entity. At block712, presentation of the entity recommendation is caused enabling a userto edit, update, and/or approve the personalized entity recommendationdefinition for delivery onto the destination webpage.

Now turning to FIG. 8, a flow diagram is illustrated showing anexemplary method 800 for delivering personalized entity recommendationdefinitions onto a destination webpage independent of destinationwebpage characteristics, at runtime. In embodiments, the method 800 isperformed by a recommendation engine, such as recommendation engine 200of FIG. 2. Initially, and as indicated at block 802, the HTML DOM of aselected baseline entity identified by an entity recommendationdefinition and located on the destination webpage is cloned. Asindicated at block 804, utilizing an entity recommendation definition,one or more entity attributes associated with a recommended entity arepopulated into the cloned HTML DOM of the baseline entity to generate apersonalized entity recommendation. As indicated at block 806,presentation of the personalized entity recommendation is caused inassociation with the destination webpage independent of destinationwebpage characteristics.

Having briefly described an overview embodiments of the presentinvention, an exemplary operating environment in which at leastexemplary embodiments of the present invention may be implemented isdescribed below in order to provide a general context for variousaspects of the present invention. Referring to FIG. 9, an exemplaryoperating environment for implementing embodiments of the describedtechnology is shown and designated generally as computing device 900.The computing device 900 is but one example of a suitable computingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the invention. Neither should thecomputing device 900 be interpreted as having any dependency orrequirement relating to any one or combination of componentsillustrated.

The invention may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Theinvention may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The invention may alsobe practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

With continued reference to FIG. 9, the computing device 900 includes abus 910 that directly or indirectly couples the following devices: amemory 912, one or more processors 914, one or more presentationcomponents 916, one or more input/output (I/O) ports 918, one or moreinput/output components 920, and an illustrative power supply 922. Thebus 910 represents what may be one or more busses (such as an addressbus, data bus, or combination thereof). Although the various blocks ofFIG. 9 are shown with lines for the sake of clarity, in reality,delineating various components is not so clear, and metaphorically, thelines would more accurately be grey and fuzzy. For example, one mayconsider a presentation component such as a display device to be an I/Ocomponent. Also, processors have memory. The inventors recognize thatsuch is the nature of the art, and reiterate that the diagram of FIG. 9is merely illustrative of an exemplary computing device that can be usedin connection with one or more embodiments of the present invention.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “hand-held device,” etc., as all are contemplatedwithin the scope of FIG. 9 and reference to “computing device.”

The computing device 900 typically includes a variety ofcomputer-readable media. Computer-readable media can be any availablemedia that can be accessed by computing device 900 and includes bothvolatile and nonvolatile media, and removable and non-removable media.By way of example, and not limitation, computer-readable media maycomprise computer storage media and communication media. Computerstorage media includes both volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tostore the desired information and which can be accessed by computingdevice 900. Computer storage media does not comprise signals per se.Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above should also be included within the scope of computer-readablemedia.

The memory 912 includes computer-storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, non-removable,or a combination thereof. Exemplary hardware devices include solid-statememory, hard drives, optical-disc drives, etc. Computing device 900includes one or more processors that read data from various entitiessuch as memory 912 or I/O components 920. Presentation component(s) 916present data indications to a user or other device. Exemplarypresentation components include a display device, speaker, printingcomponent, vibrating component, etc.

The I/O ports 918 allow computing device 900 to be logically coupled toother devices including I/O components 920, some of which may be builtin. Illustrative I/O components include a microphone, joystick, gamepad, satellite dish, scanner, printer, wireless device, etc. The I/Ocomponents 920 may provide a natural user interface (NUI) that processesair gestures, voice, or other physiological inputs generated by a user.In some instances, inputs may be transmitted to an appropriate networkelement for further processing. An NUI may implement any combination ofspeech recognition, stylus recognition, facial recognition, biometricrecognition, gesture recognition both on screen and adjacent to thescreen, air gestures, head and eye tracking, and touch recognition (asdescribed in more detail below) associated with a display of thecomputing device 900. The computing device 900 may be equipped withdepth cameras, such as stereoscopic camera systems, infrared camerasystems, RGB camera systems, touchscreen technology, and combinations ofthese, for gesture detection and recognition. Additionally, thecomputing device 900 may be equipped with accelerometers or gyroscopesthat enable detection of motion. The output of the accelerometers orgyroscopes may be provided to the display of the computing device 900 torender immersive augmented reality or virtual reality.

As can be understood, aspects of the present invention provide systems,methods, and computer-readable storage media for authoring personalizedentity recommendations. In this regard, a user selects a baseline entityon a webpage where a personalized entity recommendation is to bedelivered. An identifier of the baseline entity is recorded. A HTML DOMof the selected baseline entity is parsed into a plurality of HTMLelements. Entity attributes associated with a recommended entity aremapped to the HTML elements of the parsed HTML DOM. An entityrecommendation definition is generated utilizing the baseline entityidentifier and the mapped entity attributes. The entity recommendationdefinition is presented enabling a user to update, edit, and/or approvethe same. Aspects of the present invention further provide systems,methods, and computer-readable storage media for delivering personalizedentity recommendations. In this regard, at the time a personalizedentity recommendation is to be delivered, the HTML DOM of the selectedbaseline entity is cloned and the entity recommendation definition isutilized to populate entity attributes associated with the recommendedentity into the cloned HTML DOM creating a personalized entityrecommendation. The personalized entity recommendation then is deliveredto the destination webpage independent of the destination webpagecharacteristics.

Some specific embodiments of the invention have been described, whichare intended in all respects to be illustrative rather than restrictive.Alternative embodiments will become apparent to those of ordinary skillin the art to which the present invention pertains without departingfrom its scope.

Certain illustrated embodiments hereof are shown in the drawings andhave been described above in detail. It should be understood, however,that there is no intention to limit the invention to the specific formsdisclosed, but on the contrary, the intention is to cover allmodifications, alternative constructions, and equivalents falling withinthe spirit and scope of the invention.

It will be understood by those of ordinary skill in the art that theorder of steps shown in the methods 700 of FIG. 7 and 800 of FIG. 8 isnot meant to limit the scope of the present invention in any way and, infact, the steps may occur in a variety of different sequences withinembodiments hereof. Any and all such variations, and any combinationthereof, are contemplated to be within the scope of embodiments of thepresent invention.

What is claimed is:
 1. A computer-implemented method for definingpersonalized entity recommendations, the method comprising: receiving aselection of a baseline entity, the baseline entity having an associatedHTML DOM; recording an identifier of the baseline entity; parsing theHTML DOM of the baseline entity into a plurality of HTML elements;mapping one or more attributes associated with a recommended entity toat least a portion of the plurality of HTML elements; generating anentity recommendation definition utilizing the mapped one or moreattributes and the identifier of the baseline entity; and causingpresentation of the entity recommendation definition in association withthe destination webpage.
 2. The method of claim 1, wherein the selectedbaseline entity is associated with a destination webpage.
 3. The methodof claim 1, wherein the selected baseline entity is presented inassociation with a recommendation authoring mode.
 4. The method of claim1, wherein the entity recommendation definition is generated during aWhat You See is What You Get (“WYSWYG”) recommendation authoringexperience.
 5. The method of claim 1, wherein the entity recommendationdefinition is maintained as an entity at an edge server.
 6. The methodof claim 1, wherein at least a portion of the one or more attributesassociated with the recommended entity are derived from an entitycatalog.
 7. The method of claim 6, wherein the entity catalog isspecific to a website associated with the destination webpage.
 8. Themethod of claim 1, wherein at least a portion of the one or moreattributes associated with the recommended entity are determined basedon heuristics.
 9. The method of claim 1, further comprising: receivingone or more changes to the mapping of the one or more attributesassociated with the recommended entity to at least the portion of theplurality of HTML elements; and updating the entity recommendationdefinition in accordance with the one or more changes.
 10. The method ofclaim 1, wherein the entity recommendation definition is in JSON format.11. One or more computer storage media storing computer-useableinstructions that, when executed by one or more computing devices, causethe one or more computing devices to perform a method for deliveringpersonalized entity recommendations, the method comprising, at runtime:cloning a HTML DOM of a baseline entity identified by an entityrecommendation definition; utilizing the entity recommendationdefinition, populating one or more entity attributes associated with arecommended entity into the cloned HTML DOM of the baseline entity togenerate a personalized entity recommendation; and causing presentationof the personalized entity recommendation in association with adestination webpage.
 12. The one or more computer storage media of claim11, wherein populating the one or more entity attributes associated withthe recommended entity into the cloned HTML DOM comprises replacingentity attribute data associated with the baseline entity in the clonedHTML DOM with entity attribute data associated with the recommendedentity.
 13. The one or more computer storage media of claim 11, whereincausing presentation of the personalized entity recommendation inassociation with the destination webpage is independent of one or morestructural characteristics associated with the destination webpage. 14.The one or more computer storage media of claim 11, wherein causingpresentation of the personalized entity recommendation in associationwith the destination webpage is independent of one or more stylecharacteristics associated with the destination webpage.
 15. The one ormore computer storage media of claim 11, wherein the entityrecommendation definition is in JSON format.
 16. A computer system forimplementing a WYSWYG authoring experience for personalized entityrecommendations, the computer system comprising: a personalizedrecommendation defining manager that: (1) receives a selection of abaseline entity presented in association with a destination webpage,wherein the selected baseline entity is presented when the destinationwebpage is in an authoring mode; (2) records an identifier of thebaseline entity; (3) parses an HTML DOM of the baseline entity into aplurality of HTML elements; (4) maps one or more entity attributesassociated with a recommended entity to at least a portion of theplurality of HTML elements, wherein the mapped entity attributes arebased on at least one of attributes derived from an entity catalog andattributes determined based on heuristics; and (5) generates an entityrecommendation definition utilizing the mapped one or more entityattributes and the identifier of the baseline entity; and a personalizedrecommendation delivery component that, at runtime: (1) utilizing theentity recommendation definition, clones the HTML DOM of the baselineentity, wherein the baseline entity is presented in association with thedestination webpage; (2) utilizing the entity recommendation definition,populates one or more entity attributes associated with a recommendedentity into the cloned HTML DOM of the baseline entity to generate apersonalized entity recommendation by replacing the one or more entityattributes associated with the baseline entity with one or more entityattributes associated with a recommended entity; and (3) causespresentation of the personalized entity recommendation in associationwith the destination webpage.
 17. The computer system of claim 16,wherein the personalized recommendation defining manager causespresentation of the entity recommendation definition in association withthe destination webpage.
 18. The computer system of claim 17, whereinthe personalized recommendation defining manager receives one or morechanges to the mapping of the one or more entity attributes associatedwith the recommended entity to at least the portion of the plurality ofHTML elements, and wherein the personalized recommendation definingmanager further updates the entity recommendation definition inaccordance with the one or more changes.
 19. The computer system ofclaim 16, wherein the entity recommendation definition is in JSONformat.
 20. The computer system of claim 16, wherein the entityrecommendation definition is maintained at an edge server.